Overview

Dataset statistics

Number of variables20
Number of observations298418
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory74.8 MiB
Average record size in memory263.0 B

Variable types

Categorical4
Boolean1
Numeric12
DateTime3

Alerts

rate_code is highly overall correlated with MTA_taxHigh correlation
Dropoff_latitude is highly overall correlated with Pickup_latitudeHigh correlation
Trip_distance is highly overall correlated with Fare_amount and 1 other fieldsHigh correlation
Fare_amount is highly overall correlated with Total_amount and 1 other fieldsHigh correlation
MTA_tax is highly overall correlated with rate_codeHigh correlation
Tip_amount is highly overall correlated with Payment_typeHigh correlation
Total_amount is highly overall correlated with Fare_amount and 1 other fieldsHigh correlation
Payment_type is highly overall correlated with Tip_amountHigh correlation
Pickup_longitude is highly overall correlated with Dropoff_longitudeHigh correlation
Pickup_latitude is highly overall correlated with Dropoff_latitudeHigh correlation
Dropoff_longitude is highly overall correlated with Pickup_longitudeHigh correlation
Store_and_fwd_flag is highly imbalanced (95.0%)Imbalance
MTA_tax is highly imbalanced (92.6%)Imbalance
Payment_type is highly imbalanced (51.6%)Imbalance
Trip_type is highly imbalanced (90.5%)Imbalance
Dropoff_latitude is highly skewed (γ1 = -48.70178852)Skewed
Fare_amount is highly skewed (γ1 = 68.48804575)Skewed
Extra is highly skewed (γ1 = 22.79680221)Skewed
Tolls_amount is highly skewed (γ1 = 313.2344187)Skewed
Total_amount is highly skewed (γ1 = 51.65401062)Skewed
Pickup_longitude is highly skewed (γ1 = -316.8293882)Skewed
Pickup_latitude is highly skewed (γ1 = -52.95918059)Skewed
Dropoff_longitude is highly skewed (γ1 = -288.9412035)Skewed
Trip_distance has 8235 (2.8%) zerosZeros
Extra has 142027 (47.6%) zerosZeros
Tip_amount has 213588 (71.6%) zerosZeros
Tolls_amount has 291112 (97.6%) zerosZeros

Reproduction

Analysis started2025-11-25 13:23:51.994923
Analysis finished2025-11-25 13:24:51.338744
Duration59.34 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

vendor_id
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.5 MiB
2
236026 
1
62392 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters298418
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2236026
79.1%
162392
 
20.9%

Length

2025-11-25T18:54:51.435463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T18:54:51.565724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2236026
79.1%
162392
 
20.9%

Most occurring characters

ValueCountFrequency (%)
2236026
79.1%
162392
 
20.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)298418
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2236026
79.1%
162392
 
20.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)298418
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2236026
79.1%
162392
 
20.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)298418
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2236026
79.1%
162392
 
20.9%

Store_and_fwd_flag
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size291.6 KiB
False
296761 
True
 
1657
ValueCountFrequency (%)
False296761
99.4%
True1657
 
0.6%
2025-11-25T18:54:51.605729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

rate_code
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1250863
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-11-25T18:54:51.665757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.67654351
Coefficient of variation (CV)0.60132589
Kurtosis28.148521
Mean1.1250863
Median Absolute Deviation (MAD)0
Skewness5.4521015
Sum335746
Variance0.45771112
MonotonicityNot monotonic
2025-11-25T18:54:51.735610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1287273
96.3%
58501
 
2.8%
22138
 
0.7%
3402
 
0.1%
469
 
< 0.1%
635
 
< 0.1%
ValueCountFrequency (%)
1287273
96.3%
22138
 
0.7%
3402
 
0.1%
469
 
< 0.1%
58501
 
2.8%
635
 
< 0.1%
ValueCountFrequency (%)
635
 
< 0.1%
58501
 
2.8%
469
 
< 0.1%
3402
 
0.1%
22138
 
0.7%
1287273
96.3%

Dropoff_latitude
Real number (ℝ)

High correlation  Skewed 

Distinct61717
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.768527
Minimum25.685133
Maximum41.628765
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-11-25T18:54:51.835815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum25.685133
5-th percentile40.678139
Q140.734463
median40.76577
Q340.808352
95-th percentile40.857346
Maximum41.628765
Range15.943632
Interquartile range (IQR)0.073889732

Descriptive statistics

Standard deviation0.062869721
Coefficient of variation (CV)0.0015421141
Kurtosis11242.576
Mean40.768527
Median Absolute Deviation (MAD)0.038631439
Skewness-48.701789
Sum12166062
Variance0.0039526018
MonotonicityNot monotonic
2025-11-25T18:54:51.957098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.7581787147
 
< 0.1%
40.7581901639
 
< 0.1%
40.7581863439
 
< 0.1%
40.7743606639
 
< 0.1%
40.7683982838
 
< 0.1%
40.774318738
 
< 0.1%
40.7742538538
 
< 0.1%
40.7581634538
 
< 0.1%
40.7742881837
 
< 0.1%
40.7581367537
 
< 0.1%
Other values (61707)298028
99.9%
ValueCountFrequency (%)
25.685132981
< 0.1%
36.136684421
< 0.1%
37.372005461
< 0.1%
38.79280091
< 0.1%
38.792850491
< 0.1%
38.918952941
< 0.1%
38.927127841
< 0.1%
38.927150731
< 0.1%
40.316020971
< 0.1%
40.351222991
< 0.1%
ValueCountFrequency (%)
41.628765111
< 0.1%
41.341339111
< 0.1%
41.229537961
< 0.1%
41.18807221
< 0.1%
41.16338731
< 0.1%
41.149566651
< 0.1%
41.141288761
< 0.1%
41.132762911
< 0.1%
41.114463811
< 0.1%
41.113361361
< 0.1%

Passenger_count
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5577311
Minimum0
Maximum9
Zeros101
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-11-25T18:54:52.055405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.277056
Coefficient of variation (CV)0.81981798
Kurtosis3.5373219
Mean1.5577311
Median Absolute Deviation (MAD)0
Skewness2.2405681
Sum464855
Variance1.6308719
MonotonicityNot monotonic
2025-11-25T18:54:52.134153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1236206
79.2%
526025
 
8.7%
223048
 
7.7%
36773
 
2.3%
63498
 
1.2%
42752
 
0.9%
0101
 
< 0.1%
79
 
< 0.1%
84
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
0101
 
< 0.1%
1236206
79.2%
223048
 
7.7%
36773
 
2.3%
42752
 
0.9%
526025
 
8.7%
63498
 
1.2%
79
 
< 0.1%
84
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
92
 
< 0.1%
84
 
< 0.1%
79
 
< 0.1%
63498
 
1.2%
526025
 
8.7%
42752
 
0.9%
36773
 
2.3%
223048
 
7.7%
1236206
79.2%
0101
 
< 0.1%

Trip_distance
Real number (ℝ)

High correlation  Zeros 

Distinct2408
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.976382
Minimum0
Maximum100
Zeros8235
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-11-25T18:54:52.234695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q11.09
median1.99
Q33.84
95-th percentile8.6
Maximum100
Range100
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation2.9987373
Coefficient of variation (CV)1.0075109
Kurtosis17.016139
Mean2.976382
Median Absolute Deviation (MAD)1.11
Skewness2.834292
Sum888205.96
Variance8.9924254
MonotonicityNot monotonic
2025-11-25T18:54:52.355528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08235
 
2.8%
13071
 
1.0%
0.93006
 
1.0%
1.22952
 
1.0%
1.12906
 
1.0%
0.82821
 
0.9%
1.32767
 
0.9%
1.42744
 
0.9%
0.72555
 
0.9%
1.52435
 
0.8%
Other values (2398)264926
88.8%
ValueCountFrequency (%)
08235
2.8%
0.01306
 
0.1%
0.02241
 
0.1%
0.03209
 
0.1%
0.04154
 
0.1%
0.05122
 
< 0.1%
0.06111
 
< 0.1%
0.07100
 
< 0.1%
0.0883
 
< 0.1%
0.0990
 
< 0.1%
ValueCountFrequency (%)
1001
< 0.1%
62.181
< 0.1%
60.31
< 0.1%
58.31
< 0.1%
561
< 0.1%
54.751
< 0.1%
53.21
< 0.1%
43.891
< 0.1%
43.531
< 0.1%
42.031
< 0.1%

Fare_amount
Real number (ℝ)

High correlation  Skewed 

Distinct344
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.398314
Minimum0
Maximum2794.5
Zeros2108
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-11-25T18:54:52.478028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q16.5
median9.5
Q315.5
95-th percentile30
Maximum2794.5
Range2794.5
Interquartile range (IQR)9

Descriptive statistics

Standard deviation11.273051
Coefficient of variation (CV)0.90924065
Kurtosis15163.647
Mean12.398314
Median Absolute Deviation (MAD)4
Skewness68.488046
Sum3699880
Variance127.08168
MonotonicityNot monotonic
2025-11-25T18:54:52.745337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.514211
 
4.8%
614200
 
4.8%
5.513611
 
4.6%
713600
 
4.6%
512991
 
4.4%
7.512514
 
4.2%
811975
 
4.0%
8.510781
 
3.6%
4.510366
 
3.5%
99947
 
3.3%
Other values (334)174222
58.4%
ValueCountFrequency (%)
02108
0.7%
0.0136
 
< 0.1%
0.024
 
< 0.1%
0.035
 
< 0.1%
0.054
 
< 0.1%
0.072
 
< 0.1%
0.084
 
< 0.1%
0.091
 
< 0.1%
0.113
 
< 0.1%
0.111
 
< 0.1%
ValueCountFrequency (%)
2794.51
< 0.1%
1912.51
< 0.1%
5031
< 0.1%
4441
< 0.1%
3501
< 0.1%
2502
< 0.1%
2001
< 0.1%
184.51
< 0.1%
1802
< 0.1%
1721
< 0.1%

Extra
Real number (ℝ)

Skewed  Zeros 

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35395043
Minimum0
Maximum54.67
Zeros142027
Zeros (%)47.6%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-11-25T18:54:52.845684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q30.5
95-th percentile1
Maximum54.67
Range54.67
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.44915874
Coefficient of variation (CV)1.2689877
Kurtosis2033.202
Mean0.35395043
Median Absolute Deviation (MAD)0.5
Skewness22.796802
Sum105625.18
Variance0.20174358
MonotonicityNot monotonic
2025-11-25T18:54:52.951238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0142027
47.6%
0.5103004
34.5%
153315
 
17.9%
812
 
< 0.1%
55
 
< 0.1%
7.55
 
< 0.1%
105
 
< 0.1%
124
 
< 0.1%
24
 
< 0.1%
63
 
< 0.1%
Other values (25)34
 
< 0.1%
ValueCountFrequency (%)
0142027
47.6%
0.012
 
< 0.1%
0.023
 
< 0.1%
0.5103004
34.5%
0.511
 
< 0.1%
0.521
 
< 0.1%
0.61
 
< 0.1%
0.751
 
< 0.1%
153315
 
17.9%
1.52
 
< 0.1%
ValueCountFrequency (%)
54.671
< 0.1%
452
< 0.1%
421
< 0.1%
34.331
< 0.1%
30.51
< 0.1%
302
< 0.1%
251
< 0.1%
231
< 0.1%
22.221
< 0.1%
171
< 0.1%

MTA_tax
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.1 MiB
0.5
290851 
0.0
 
7558
0.4
 
4
0.25
 
3
0.6
 
2

Length

Max length4
Median length3
Mean length3.0000101
Min length3

Characters and Unicode

Total characters895257
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5290851
97.5%
0.07558
 
2.5%
0.44
 
< 0.1%
0.253
 
< 0.1%
0.62
 
< 0.1%

Length

2025-11-25T18:54:53.060966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T18:54:53.134585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.5290851
97.5%
0.07558
 
2.5%
0.44
 
< 0.1%
0.253
 
< 0.1%
0.62
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0305976
34.2%
.298418
33.3%
5290854
32.5%
44
 
< 0.1%
23
 
< 0.1%
62
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)895257
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0305976
34.2%
.298418
33.3%
5290854
32.5%
44
 
< 0.1%
23
 
< 0.1%
62
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)895257
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0305976
34.2%
.298418
33.3%
5290854
32.5%
44
 
< 0.1%
23
 
< 0.1%
62
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)895257
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0305976
34.2%
.298418
33.3%
5290854
32.5%
44
 
< 0.1%
23
 
< 0.1%
62
 
< 0.1%

Tip_amount
Real number (ℝ)

High correlation  Zeros 

Distinct871
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.88196513
Minimum0
Maximum210.08
Zeros213588
Zeros (%)71.6%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-11-25T18:54:53.236403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4.7
Maximum210.08
Range210.08
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.1264953
Coefficient of variation (CV)2.4110878
Kurtosis989.78495
Mean0.88196513
Median Absolute Deviation (MAD)0
Skewness15.872708
Sum263194.27
Variance4.5219824
MonotonicityNot monotonic
2025-11-25T18:54:53.354457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0213588
71.6%
19388
 
3.1%
28508
 
2.9%
1.54565
 
1.5%
33975
 
1.3%
2.52397
 
0.8%
42048
 
0.7%
51836
 
0.6%
1.81555
 
0.5%
1.41465
 
0.5%
Other values (861)49093
 
16.5%
ValueCountFrequency (%)
0213588
71.6%
0.01130
 
< 0.1%
0.0239
 
< 0.1%
0.0317
 
< 0.1%
0.044
 
< 0.1%
0.0530
 
< 0.1%
0.0610
 
< 0.1%
0.073
 
< 0.1%
0.0824
 
< 0.1%
0.098
 
< 0.1%
ValueCountFrequency (%)
210.081
< 0.1%
2001
< 0.1%
1751
< 0.1%
1501
< 0.1%
113.771
< 0.1%
1101
< 0.1%
1001
< 0.1%
99.451
< 0.1%
961
< 0.1%
88.251
< 0.1%

Tolls_amount
Real number (ℝ)

Skewed  Zeros 

Distinct86
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14094438
Minimum0
Maximum950
Zeros291112
Zeros (%)97.6%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-11-25T18:54:53.465600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum950
Range950
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.392929
Coefficient of variation (CV)16.977825
Kurtosis115511.92
Mean0.14094438
Median Absolute Deviation (MAD)0
Skewness313.23442
Sum42060.34
Variance5.7261091
MonotonicityNot monotonic
2025-11-25T18:54:53.586846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0291112
97.6%
5.336482
 
2.2%
2.44265
 
0.1%
7.5106
 
< 0.1%
10.6684
 
< 0.1%
8.2567
 
< 0.1%
951
 
< 0.1%
1145
 
< 0.1%
10.2534
 
< 0.1%
224
 
< 0.1%
Other values (76)148
 
< 0.1%
ValueCountFrequency (%)
0291112
97.6%
0.012
 
< 0.1%
0.021
 
< 0.1%
0.091
 
< 0.1%
0.11
 
< 0.1%
0.516
 
< 0.1%
14
 
< 0.1%
1.21
 
< 0.1%
1.251
 
< 0.1%
1.51
 
< 0.1%
ValueCountFrequency (%)
9501
< 0.1%
7501
< 0.1%
731
< 0.1%
46.51
< 0.1%
451
< 0.1%
33.161
< 0.1%
331
< 0.1%
28.251
< 0.1%
27.331
< 0.1%
241
< 0.1%

Total_amount
Real number (ℝ)

High correlation  Skewed 

Distinct2392
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.262719
Minimum0
Maximum2796
Zeros1980
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-11-25T18:54:53.699972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q17.5
median11
Q317.5
95-th percentile34.88
Maximum2796
Range2796
Interquartile range (IQR)10

Descriptive statistics

Standard deviation12.612077
Coefficient of variation (CV)0.88426881
Kurtosis9827.2323
Mean14.262719
Median Absolute Deviation (MAD)4
Skewness51.654011
Sum4256251.9
Variance159.06449
MonotonicityNot monotonic
2025-11-25T18:54:53.815202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
712616
 
4.2%
812137
 
4.1%
6.511769
 
3.9%
7.511446
 
3.8%
610916
 
3.7%
910171
 
3.4%
8.510054
 
3.4%
109534
 
3.2%
9.59108
 
3.1%
5.59046
 
3.0%
Other values (2382)191621
64.2%
ValueCountFrequency (%)
01980
0.7%
0.0131
 
< 0.1%
0.023
 
< 0.1%
0.034
 
< 0.1%
0.054
 
< 0.1%
0.071
 
< 0.1%
0.083
 
< 0.1%
0.092
 
< 0.1%
0.110
 
< 0.1%
0.126
 
< 0.1%
ValueCountFrequency (%)
27961
< 0.1%
19141
< 0.1%
9611
< 0.1%
770.51
< 0.1%
504.51
< 0.1%
445.51
< 0.1%
3501
< 0.1%
348.51
< 0.1%
250.52
< 0.1%
228.081
< 0.1%

Payment_type
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.5 MiB
2
199290 
1
97253 
3
 
1110
4
 
765

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters298418
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2199290
66.8%
197253
32.6%
31110
 
0.4%
4765
 
0.3%

Length

2025-11-25T18:54:53.929026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T18:54:54.007553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2199290
66.8%
197253
32.6%
31110
 
0.4%
4765
 
0.3%

Most occurring characters

ValueCountFrequency (%)
2199290
66.8%
197253
32.6%
31110
 
0.4%
4765
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)298418
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2199290
66.8%
197253
32.6%
31110
 
0.4%
4765
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)298418
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2199290
66.8%
197253
32.6%
31110
 
0.4%
4765
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)298418
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2199290
66.8%
197253
32.6%
31110
 
0.4%
4765
 
0.3%

Trip_type
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.5 MiB
2
294783 
1
 
3635

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters298418
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2294783
98.8%
13635
 
1.2%

Length

2025-11-25T18:54:54.105448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T18:54:54.171372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2294783
98.8%
13635
 
1.2%

Most occurring characters

ValueCountFrequency (%)
2294783
98.8%
13635
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)298418
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2294783
98.8%
13635
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)298418
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2294783
98.8%
13635
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)298418
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2294783
98.8%
13635
 
1.2%

Pickup_longitude
Real number (ℝ)

High correlation  Skewed 

Distinct23898
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.925267
Minimum-121.92611
Maximum-73.020638
Zeros0
Zeros (%)0.0%
Negative298418
Negative (%)100.0%
Memory size2.3 MiB
2025-11-25T18:54:54.257131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-121.92611
5-th percentile-73.978615
Q1-73.953369
median-73.938515
Q3-73.902977
95-th percentile-73.844124
Maximum-73.020638
Range48.905472
Interquartile range (IQR)0.05039215

Descriptive statistics

Standard deviation0.12421378
Coefficient of variation (CV)-0.0016802615
Kurtosis115530.23
Mean-73.925267
Median Absolute Deviation (MAD)0.02030945
Skewness-316.82939
Sum-22060630
Variance0.015429063
MonotonicityNot monotonic
2025-11-25T18:54:54.376646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.84427643478
 
0.2%
-73.8442688471
 
0.2%
-73.84425354442
 
0.1%
-73.84429169408
 
0.1%
-73.84423828403
 
0.1%
-73.84429932379
 
0.1%
-73.84428406345
 
0.1%
-73.84426117319
 
0.1%
-73.84423065305
 
0.1%
-73.84424591292
 
0.1%
Other values (23888)294576
98.7%
ValueCountFrequency (%)
-121.92610931
< 0.1%
-115.17911
< 0.1%
-80.313911441
< 0.1%
-77.060203551
< 0.1%
-77.05946351
< 0.1%
-76.979789731
< 0.1%
-76.979667661
< 0.1%
-76.9589921
< 0.1%
-75.590736391
< 0.1%
-74.43072511
< 0.1%
ValueCountFrequency (%)
-73.020637511
< 0.1%
-73.192863461
< 0.1%
-73.240859991
< 0.1%
-73.252822881
< 0.1%
-73.321189881
< 0.1%
-73.426765441
< 0.1%
-73.523345951
< 0.1%
-73.524765011
< 0.1%
-73.52951051
< 0.1%
-73.537544251
< 0.1%

Pickup_latitude
Real number (ℝ)

High correlation  Skewed 

Distinct51729
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.774183
Minimum25.684929
Maximum41.628765
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2025-11-25T18:54:54.485225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum25.684929
5-th percentile40.687202
Q140.734811
median40.775486
Q340.810806
95-th percentile40.855912
Maximum41.628765
Range15.943836
Interquartile range (IQR)0.075995445

Descriptive statistics

Standard deviation0.061193477
Coefficient of variation (CV)0.0015007898
Kurtosis12545.94
Mean40.774183
Median Absolute Deviation (MAD)0.036024094
Skewness-52.959181
Sum12167750
Variance0.0037446416
MonotonicityNot monotonic
2025-11-25T18:54:54.614337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.72133636348
 
0.1%
40.72132874342
 
0.1%
40.72132111300
 
0.1%
40.72135162285
 
0.1%
40.72134018270
 
0.1%
40.72135544254
 
0.1%
40.72136688239
 
0.1%
40.72133255236
 
0.1%
40.72131348228
 
0.1%
40.72134399213
 
0.1%
Other values (51719)295703
99.1%
ValueCountFrequency (%)
25.684928891
< 0.1%
36.137096411
< 0.1%
37.372001651
< 0.1%
38.79290391
< 0.1%
38.792915341
< 0.1%
38.91894151
< 0.1%
38.927310942
< 0.1%
40.469348911
< 0.1%
40.520408631
< 0.1%
40.573371891
< 0.1%
ValueCountFrequency (%)
41.628765111
< 0.1%
41.132759091
< 0.1%
41.037860871
< 0.1%
40.994743351
< 0.1%
40.980632781
< 0.1%
40.979354861
< 0.1%
40.97023011
< 0.1%
40.966800691
< 0.1%
40.965015411
< 0.1%
40.96091081
< 0.1%

Dropoff_longitude
Real number (ℝ)

High correlation  Skewed 

Distinct31326
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.926735
Minimum-121.9259
Maximum-69.347466
Zeros0
Zeros (%)0.0%
Negative298418
Negative (%)100.0%
Memory size2.3 MiB
2025-11-25T18:54:54.734525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-121.9259
5-th percentile-73.992905
Q1-73.960419
median-73.938316
Q3-73.89711
95-th percentile-73.829468
Maximum-69.347466
Range52.578438
Interquartile range (IQR)0.06330871

Descriptive statistics

Standard deviation0.12805712
Coefficient of variation (CV)-0.0017322167
Kurtosis102264.39
Mean-73.926735
Median Absolute Deviation (MAD)0.02974701
Skewness-288.9412
Sum-22061068
Variance0.016398626
MonotonicityNot monotonic
2025-11-25T18:54:54.855525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.939109866
 
< 0.1%
-73.9391784766
 
< 0.1%
-73.9391403261
 
< 0.1%
-73.9374160860
 
< 0.1%
-73.9390716660
 
< 0.1%
-73.9375686659
 
< 0.1%
-73.9374084558
 
< 0.1%
-73.9391632158
 
< 0.1%
-73.9391326956
 
< 0.1%
-73.9490203955
 
< 0.1%
Other values (31316)297819
99.8%
ValueCountFrequency (%)
-121.92590331
< 0.1%
-115.17933651
< 0.1%
-80.314300541
< 0.1%
-77.06029511
< 0.1%
-77.060203551
< 0.1%
-76.979812621
< 0.1%
-76.979560851
< 0.1%
-76.958976751
< 0.1%
-75.590744021
< 0.1%
-74.457389831
< 0.1%
ValueCountFrequency (%)
-69.347465521
< 0.1%
-72.938034061
< 0.1%
-73.019729611
< 0.1%
-73.16787721
< 0.1%
-73.191406251
< 0.1%
-73.240867611
< 0.1%
-73.252807621
< 0.1%
-73.267318731
< 0.1%
-73.293144231
< 0.1%
-73.293191
< 0.1%
Distinct4534
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
Minimum2025-11-25 00:00:01
Maximum2025-11-25 23:58:39
Invalid dates0
Invalid dates (%)0.0%
2025-11-25T18:54:54.978674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:55.096344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct290160
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
Minimum2013-01-09 00:03:30
Maximum2013-12-31 23:59:57
Invalid dates0
Invalid dates (%)0.0%
2025-11-25T18:54:55.206165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:55.336446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct290640
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
Minimum2013-01-09 00:13:56
Maximum2014-01-01 21:36:54
Invalid dates0
Invalid dates (%)0.0%
2025-11-25T18:54:55.627286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:55.748831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2025-11-25T18:54:48.338379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:30.028726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:31.667501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:33.370545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:34.968246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:36.675413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:38.285820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:39.874187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:41.668076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:43.242619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:44.860359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:46.466062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:48.478176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:30.169933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:31.801530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:33.496292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:35.099464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:36.805796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:38.420389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:40.000458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:41.798659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:43.376104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:44.992465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:46.613479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:48.623267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:30.308332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:31.956219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:33.637017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:35.252036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:36.948267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:38.556691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:40.147462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:41.938968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:43.516674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:45.133370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:46.759017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:48.760407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:30.440786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:32.092513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:33.770434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:35.421037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:37.078041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:38.681712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:40.430880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:42.067395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:43.649668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:45.258042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:46.901511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:48.904919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:30.579577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:32.231916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:33.898673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:35.538404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:37.206921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:38.820745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:40.556619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:42.196680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:43.778924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:45.388814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:47.047306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:49.047485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:30.739296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:32.370315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:34.026860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:35.670335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:37.341999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:38.948528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:40.686686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:42.326493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:43.919494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:45.523850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:47.338928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:49.194825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:30.877233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:32.509224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:34.162025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:35.800055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:37.470137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:39.077836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:40.807712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:42.457526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:44.048363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:45.659269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:47.478470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:49.334827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:31.007153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:32.646488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:34.293951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:35.949303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:37.599703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:39.204501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:40.935679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:42.583025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:44.186049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:45.795429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:47.626235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:49.471309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:31.138640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:32.781420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:34.427880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:36.101102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:37.730526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:39.337926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:41.057548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:42.706549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:44.306697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:45.929670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:47.770120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:49.609251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:31.268782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:32.927214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:34.558331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:36.238234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:37.867860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:39.473450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:41.189057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:42.835756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:44.440965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:46.068578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:47.915293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:49.745896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:31.398516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:33.071904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:34.690130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:36.392401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:38.000897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:39.606083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:41.399734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:42.966377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:44.577082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:46.194122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:48.047901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:49.887070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:31.534594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:33.218865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:34.835336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:36.538971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:38.146920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:39.745634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:41.540240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:43.110995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:44.716262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:46.335004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-25T18:54:48.198132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-25T18:54:55.866666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
vendor_idrate_codeDropoff_latitudePassenger_countTrip_distanceFare_amountExtraMTA_taxTip_amountTolls_amountTotal_amountPayment_typeTrip_typePickup_longitudePickup_latitudeDropoff_longitude
vendor_id1.000-0.0860.0320.1030.0070.0240.0120.150-0.0040.0020.022-0.052-0.0570.0230.0430.019
rate_code-0.0861.0000.073-0.0240.0540.059-0.128-0.785-0.0110.0160.0440.0620.0070.0360.0840.039
Dropoff_latitude0.0320.0731.000-0.043-0.183-0.153-0.071-0.062-0.140-0.013-0.1660.1240.0110.2050.8600.207
Passenger_count0.103-0.024-0.0431.0000.0050.0090.0240.0200.001-0.0010.009-0.006-0.0230.005-0.0480.003
Trip_distance0.0070.054-0.1830.0051.0000.727-0.034-0.0250.4030.1340.742-0.203-0.004-0.000-0.0210.013
Fare_amount0.0240.059-0.1530.0090.7271.000-0.035-0.0010.3260.1010.967-0.169-0.005-0.008-0.032-0.008
Extra0.012-0.128-0.0710.024-0.034-0.0351.0000.093-0.003-0.0080.003-0.015-0.007-0.000-0.0930.003
MTA_tax0.150-0.785-0.0620.020-0.025-0.0010.0931.0000.017-0.0140.009-0.070-0.011-0.020-0.066-0.016
Tip_amount-0.004-0.011-0.1400.0010.4030.326-0.0030.0171.0000.0820.475-0.579-0.003-0.047-0.057-0.050
Tolls_amount0.0020.016-0.013-0.0010.1340.101-0.008-0.0140.0821.0000.293-0.031-0.000-0.0030.0100.007
Total_amount0.0220.044-0.1660.0090.7420.9670.0030.0090.4750.2931.000-0.255-0.006-0.016-0.040-0.014
Payment_type-0.0520.0620.124-0.006-0.203-0.169-0.015-0.070-0.579-0.031-0.2551.0000.0060.0710.0710.087
Trip_type-0.0570.0070.011-0.023-0.004-0.005-0.007-0.011-0.003-0.000-0.0060.0061.000-0.0070.009-0.005
Pickup_longitude0.0230.0360.2050.005-0.000-0.008-0.000-0.020-0.047-0.003-0.0160.071-0.0071.0000.2060.956
Pickup_latitude0.0430.0840.860-0.048-0.021-0.032-0.093-0.066-0.0570.010-0.0400.0710.0090.2061.0000.193
Dropoff_longitude0.0190.0390.2070.0030.013-0.0080.003-0.016-0.0500.007-0.0140.087-0.0050.9560.1931.000
2025-11-25T18:54:56.068681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Dropoff_latitudeDropoff_longitudeExtraFare_amountMTA_taxPassenger_countPayment_typePickup_latitudePickup_longitudeStore_and_fwd_flagTip_amountTolls_amountTotal_amountTrip_distanceTrip_typerate_codevendor_id
Dropoff_latitude1.0000.096-0.126-0.1780.387-0.0700.0000.8220.0390.000-0.173-0.025-0.201-0.1740.0000.0590.000
Dropoff_longitude0.0961.0000.005-0.1470.3540.0130.0000.0090.6930.000-0.2980.058-0.178-0.1320.0000.1150.000
Extra-0.1260.0051.000-0.0310.0220.0280.036-0.158-0.0210.0000.027-0.0400.041-0.0150.000-0.1870.025
Fare_amount-0.178-0.147-0.0311.0000.0000.0220.000-0.049-0.0550.0000.2980.2160.9830.9120.0000.0600.000
MTA_tax0.3870.3540.0220.0001.0000.0660.0590.3870.0050.0130.0180.0070.0050.0290.0100.4050.150
Passenger_count-0.0700.0130.0280.0220.0661.0000.020-0.0800.0240.044-0.001-0.0000.0220.0150.0260.0090.225
Payment_type0.0000.0000.0360.0000.0590.0201.0000.0000.0000.0240.0160.0000.0000.0480.0090.0610.155
Pickup_latitude0.8220.009-0.158-0.0490.387-0.0800.0001.0000.0480.000-0.1030.033-0.073-0.0440.0000.1050.000
Pickup_longitude0.0390.693-0.021-0.0550.0050.0240.0000.0481.0000.000-0.267-0.027-0.091-0.0450.0000.0860.000
Store_and_fwd_flag0.0000.0000.0000.0000.0130.0440.0240.0000.0001.0000.0030.0000.0000.0120.0080.0090.145
Tip_amount-0.173-0.2980.0270.2980.018-0.0010.016-0.103-0.2670.0031.0000.1280.4180.2940.000-0.0590.006
Tolls_amount-0.0250.058-0.0400.2160.007-0.0000.0000.033-0.0270.0000.1281.0000.2390.2230.0000.0950.000
Total_amount-0.201-0.1780.0410.9830.0050.0220.000-0.073-0.0910.0000.4180.2391.0000.9000.0000.0410.000
Trip_distance-0.174-0.132-0.0150.9120.0290.0150.048-0.044-0.0450.0120.2940.2230.9001.0000.011-0.0090.004
Trip_type0.0000.0000.0000.0000.0100.0260.0090.0000.0000.0080.0000.0000.0000.0111.0000.0080.057
rate_code0.0590.115-0.1870.0600.4050.0090.0610.1050.0860.009-0.0590.0950.041-0.0090.0081.0000.090
vendor_id0.0000.0000.0250.0000.1500.2250.1550.0000.0000.1450.0060.0000.0000.0040.0570.0901.000

Missing values

2025-11-25T18:54:50.068100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-25T18:54:50.525889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

vendor_idStore_and_fwd_flagrate_codeDropoff_latitudePassenger_countTrip_distanceFare_amountExtraMTA_taxTip_amountTolls_amountTotal_amountPayment_typeTrip_typePickup_longitudePickup_latitudeDropoff_longitudeDifference_between_p_d_timepickup_datetimedropoff_datetime
01N140.75908711.809.00.50.50.00.010.022-73.90036840.745861-73.92593400:49:522013-03-11 01:06:002013-03-11 01:55:52
12N140.70106916.3622.00.50.50.00.023.022-73.85224240.715996-73.92247023:51:002013-11-30 00:04:002013-12-01 23:55:00
21N140.75623314.6016.00.50.53.40.020.412-73.95442240.730217-73.96766723:42:002013-11-30 00:05:002013-12-01 23:47:00
32N140.68071412.6910.01.00.50.00.011.522-73.83012440.713730-73.81131700:51:272013-11-12 01:03:112013-11-13 01:54:38
42N140.71669417.2131.00.50.54.00.036.012-73.92450740.761822-73.99688023:33:002013-10-12 00:09:002013-10-13 23:42:00
52N140.74507111.768.00.50.50.00.09.022-73.91500940.763996-73.91943400:48:512013-11-30 01:09:462013-12-01 01:58:37
62N140.76143616.8024.00.50.50.00.025.022-73.93782040.818451-73.98408523:53:002013-10-31 00:01:002013-11-01 23:54:00
72N140.75918252.8611.00.50.50.00.012.012-73.91916740.758801-73.87561023:47:002013-11-12 00:00:002013-11-13 23:47:00
82N140.73143814.0626.00.50.55.40.032.412-73.95117940.714035-74.00651623:38:142013-10-12 00:04:372013-10-13 23:42:51
91N140.66970813.7015.00.50.50.00.016.012-73.94879940.714195-73.93105323:32:002013-11-30 00:14:002013-12-01 23:46:00
vendor_idStore_and_fwd_flagrate_codeDropoff_latitudePassenger_countTrip_distanceFare_amountExtraMTA_taxTip_amountTolls_amountTotal_amountPayment_typeTrip_typePickup_longitudePickup_latitudeDropoff_longitudeDifference_between_p_d_timepickup_datetimedropoff_datetime
2984082N140.74897811.4311.00.00.50.00.0011.5022-73.87117040.733967-73.87940200:16:192013-11-28 11:10:272013-11-28 11:26:46
2984092N140.77966714.6816.00.00.53.20.0019.7012-73.95947340.808659-73.96131100:11:522013-11-18 22:45:482013-11-18 22:57:40
2984101N140.78311516.1018.01.00.50.00.0019.5022-73.94253540.841541-73.94422100:10:362013-10-19 10:46:092013-10-19 10:56:45
2984112N140.75740435.0817.00.00.50.05.3322.8312-73.93885040.804958-73.90296200:15:452013-12-26 09:07:312013-12-26 09:23:16
2984122N140.82410012.7612.50.00.50.00.0013.0022-73.94576340.807381-73.90887500:14:452013-03-12 12:13:442013-03-12 12:28:29
2984132N140.67638811.778.00.50.50.00.009.0022-73.99329440.687912-73.96703300:07:492013-04-12 00:39:552013-04-12 00:47:44
2984141N140.84323912.2010.00.50.50.00.0011.0022-73.93828640.846970-73.90593000:10:112013-12-22 00:53:022013-12-22 01:03:13
2984152N140.68734711.8110.00.00.52.00.0012.5012-73.98687040.702442-73.97969800:12:382013-12-21 13:13:142013-12-21 13:25:52
2984162N140.72740250.814.50.50.51.10.006.6012-73.95768740.717800-73.95718400:03:132013-08-28 20:34:452013-08-28 20:37:58
2984171N140.76550311.506.00.00.50.00.006.5022-73.91767940.770012-73.89041100:02:252013-03-10 10:30:582013-03-10 10:33:23